Early approaches of language modeling for code-switched text included class-based \(n\) -gram models [1]}, factored language models that exploited a large number of syntactic and semantic features [2]}, and recurrent neural language models [3]} for CS text. All these approaches relied on access to real CS text to train the language models. Towards alleviating this dependence on real CS text, there has been prior work on learning code-switched language models from bilingual data [4]}, [5]}, [6]} and a more recent direction that explores the possibility of generating synthetic CS sentences. [7]} presents a technique to generate synthetic CS text that grammatically adheres to a linguistic theory of code-switching known as the equivalence constraint (EC) theory [8]}, [9]}. lee-li-2020-modeling proposed a bilingual attention language model for CS text trained solely using a parallel corpus.
